Abstract

Representative data on waste generation is an important pre-requisite in the formulation of sustainable waste management policies. In relation to this, data on the quantities and composition of waste arising from 681 English households during the period 2000 – 2004 was analysed under contract to the UK Department for Environment, Food and Rural Affairs. The data was self-recorded by each household as part of a tutor-marked assignment completed by students on The Open University's course 'Environmental Control and Public Health'. Waste was sorted into 15 categories and the quantity of each category of waste arising over a four-week monitoring period was recorded. In addition, each household was asked to complete a questionnaire profiling its social composition and waste disposal practices.
The arithmetic mean of the waste arisings (22.5 kg/hh/wk) exceeded the median (15.0 kg/hh/wk) by 50%. The size of the skew statistic (21.5) provided confirmation that the frequency distribution of waste generation rate data was positively skewed about the mean. The distribution also lacked the necessary curvature. The distribution was more peaked than that of a normal distribution. This was determined from visual examination and analysis of the kurtosis statistic (505.4). The Kolmogorov-Smirnov test for goodness of fit confirmed the lack of normality in the data.
A less-skewed set of waste generation rate data was obtained when households reporting irregularly-occurring waste generating activities during the four-week monitoring period were excluded. Even so, a significant degree of skew and kurtosis remained among the remaining group of households, and the Kolmogorov-Smirnov test indicated that the log-normal model provided a best-fit for the data.
The study concludes that temporal variations in the rate of waste generated by individual households have only a minor impact on the distribution of data about the sample mean and that the lack of normality in waste generation rate data acquired at household-level reflects differences in the underlying rate of waste generation. It also finds that very high rates of waste generation, often the result of irregularly-occurring, atypical episodes of waste generating activity, generate outliers in an otherwise log-normally distributed set of data. This permits the use of statistical methods which have been developed in order to estimate more accurately population means and associated confidence intervals derived from non-normal data. The findings can lead to sustainable waste management.